import pandas as pd #pandas (pd): Data manipulation ke liye use hota hai, jaise CSV file load karna aur process karna.
import plotly.express as px #plotly.express (px): Data visualization library jo easy aur quick plots banata hai.
import plotly.graph_objects as go #plotly.graph_objects (go): Advanced and customizable graphs banane ke liye
import plotly.io as pio #plotly.io (pio): Graph templates ko customize karne ke liye
import plotly.colors as colors
pio.templates.default = "plotly_white" #pio.templates.default = "plotly_white": Default theme white rakha gaya hai graphs ke liye
data = pd.read_csv("Sample - Superstore.csv", encoding='latin-1') #encoding='latin-1': Special characters ko properly read karne ke liye encoding use hui hai
data
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Postal Code | Region | Product ID | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-BO-10001798 | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 |
| 1 | 2 | CA-2016-152156 | 11/8/2016 | 11/11/2016 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | 42420 | South | FUR-CH-10000454 | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 |
| 2 | 3 | CA-2016-138688 | 6/12/2016 | 6/16/2016 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | 90036 | West | OFF-LA-10000240 | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 |
| 3 | 4 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | FUR-TA-10000577 | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 |
| 4 | 5 | US-2015-108966 | 10/11/2015 | 10/18/2015 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | 33311 | South | OFF-ST-10000760 | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 9989 | 9990 | CA-2014-110422 | 1/21/2014 | 1/23/2014 | Second Class | TB-21400 | Tom Boeckenhauer | Consumer | United States | Miami | ... | 33180 | South | FUR-FU-10001889 | Furniture | Furnishings | Ultra Door Pull Handle | 25.2480 | 3 | 0.20 | 4.1028 |
| 9990 | 9991 | CA-2017-121258 | 2/26/2017 | 3/3/2017 | Standard Class | DB-13060 | Dave Brooks | Consumer | United States | Costa Mesa | ... | 92627 | West | FUR-FU-10000747 | Furniture | Furnishings | Tenex B1-RE Series Chair Mats for Low Pile Car... | 91.9600 | 2 | 0.00 | 15.6332 |
| 9991 | 9992 | CA-2017-121258 | 2/26/2017 | 3/3/2017 | Standard Class | DB-13060 | Dave Brooks | Consumer | United States | Costa Mesa | ... | 92627 | West | TEC-PH-10003645 | Technology | Phones | Aastra 57i VoIP phone | 258.5760 | 2 | 0.20 | 19.3932 |
| 9992 | 9993 | CA-2017-121258 | 2/26/2017 | 3/3/2017 | Standard Class | DB-13060 | Dave Brooks | Consumer | United States | Costa Mesa | ... | 92627 | West | OFF-PA-10004041 | Office Supplies | Paper | It's Hot Message Books with Stickers, 2 3/4" x 5" | 29.6000 | 4 | 0.00 | 13.3200 |
| 9993 | 9994 | CA-2017-119914 | 5/4/2017 | 5/9/2017 | Second Class | CC-12220 | Chris Cortes | Consumer | United States | Westminster | ... | 92683 | West | OFF-AP-10002684 | Office Supplies | Appliances | Acco 7-Outlet Masterpiece Power Center, Wihtou... | 243.1600 | 2 | 0.00 | 72.9480 |
9994 rows × 21 columns
data.describe()
| Row ID | Postal Code | Sales | Quantity | Discount | Profit | |
|---|---|---|---|---|---|---|
| count | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 | 9994.000000 |
| mean | 4997.500000 | 55190.379428 | 229.858001 | 3.789574 | 0.156203 | 28.656896 |
| std | 2885.163629 | 32063.693350 | 623.245101 | 2.225110 | 0.206452 | 234.260108 |
| min | 1.000000 | 1040.000000 | 0.444000 | 1.000000 | 0.000000 | -6599.978000 |
| 25% | 2499.250000 | 23223.000000 | 17.280000 | 2.000000 | 0.000000 | 1.728750 |
| 50% | 4997.500000 | 56430.500000 | 54.490000 | 3.000000 | 0.200000 | 8.666500 |
| 75% | 7495.750000 | 90008.000000 | 209.940000 | 5.000000 | 0.200000 | 29.364000 |
| max | 9994.000000 | 99301.000000 | 22638.480000 | 14.000000 | 0.800000 | 8399.976000 |
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null object 3 Ship Date 9994 non-null object 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: float64(3), int64(3), object(15) memory usage: 1.6+ MB
data['Order Date']=pd.to_datetime(data['Order Date'])
data['Ship Date'] = pd.to_datetime(data['Ship Date'])
#Date Conversion: Order Date aur Ship Date columns ko datetime format me convert kiya gaya hai for date-based analysis.
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9994 entries, 0 to 9993 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Row ID 9994 non-null int64 1 Order ID 9994 non-null object 2 Order Date 9994 non-null datetime64[ns] 3 Ship Date 9994 non-null datetime64[ns] 4 Ship Mode 9994 non-null object 5 Customer ID 9994 non-null object 6 Customer Name 9994 non-null object 7 Segment 9994 non-null object 8 Country 9994 non-null object 9 City 9994 non-null object 10 State 9994 non-null object 11 Postal Code 9994 non-null int64 12 Region 9994 non-null object 13 Product ID 9994 non-null object 14 Category 9994 non-null object 15 Sub-Category 9994 non-null object 16 Product Name 9994 non-null object 17 Sales 9994 non-null float64 18 Quantity 9994 non-null int64 19 Discount 9994 non-null float64 20 Profit 9994 non-null float64 dtypes: datetime64[ns](2), float64(3), int64(3), object(13) memory usage: 1.6+ MB
data['Order Month'] = data['Order Date'].dt.month
data['Order Year'] = data['Order Date'].dt.year
data['Order Day of Week'] = data['Order Date'].dt.dayofweek
#Order Month: Order date se month extract karte hain.
#Order Year: Order date se year extract hota hai.
#Order Day of Week: Week ka day (0 for Monday, 6 for Sunday) extract kiya gaya hai
data.head()
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | Order Month | Order Year | Order Day of Week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 | 11 | 2016 | 1 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 | 11 | 2016 | 1 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 | 6 | 2016 | 6 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 | 10 | 2015 | 6 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 | 10 | 2015 | 6 |
5 rows × 24 columns
sales_by_month = data.groupby('Order Month')['Sales'].sum().reset_index()
sales_by_month
| Order Month | Sales | |
|---|---|---|
| 0 | 1 | 94924.8356 |
| 1 | 2 | 59751.2514 |
| 2 | 3 | 205005.4888 |
| 3 | 4 | 137762.1286 |
| 4 | 5 | 155028.8117 |
| 5 | 6 | 152718.6793 |
| 6 | 7 | 147238.0970 |
| 7 | 8 | 159044.0630 |
| 8 | 9 | 307649.9457 |
| 9 | 10 | 200322.9847 |
| 10 | 11 | 352461.0710 |
| 11 | 12 | 325293.5035 |
fig = px.line(sales_by_month,
x='Order Month',
y='Sales',
title='Monthly Sales Analysis')
fig.show()
#Data Grouping:
#data.groupby('Order Month')['Sales'].sum() se har month ki total sales nikalte hain.
#.reset_index() data ko structured format me rakhta hai.
#px.line: Monthly sales trend show karne ke liye line chart banaya gaya hai.
#fig.show(): Graph display karta hai.
sales_by_category = data.groupby('Category')['Sales'].sum().reset_index()
sales_by_category
| Category | Sales | |
|---|---|---|
| 0 | Furniture | 741999.7953 |
| 1 | Office Supplies | 719047.0320 |
| 2 | Technology | 836154.0330 |
fig = px.pie(sales_by_category,
values='Sales',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Sales Analysis by Category', title_font=dict(size=24))
fig.show()
#groupby('Category'): Category-wise sales nikalte hain.
#Pie Chart:
#px.pie: Sales proportions ko pie chart me show karta hai.
#hole=0.5: Donut-style chart banata hai.
#Pastel Colors: Chart me soft color palette use kiya gaya hai.
sales_by_subcategory = data.groupby('Sub-Category')['Sales'].sum().reset_index()
sales_by_subcategory
| Sub-Category | Sales | |
|---|---|---|
| 0 | Accessories | 167380.3180 |
| 1 | Appliances | 107532.1610 |
| 2 | Art | 27118.7920 |
| 3 | Binders | 203412.7330 |
| 4 | Bookcases | 114879.9963 |
| 5 | Chairs | 328449.1030 |
| 6 | Copiers | 149528.0300 |
| 7 | Envelopes | 16476.4020 |
| 8 | Fasteners | 3024.2800 |
| 9 | Furnishings | 91705.1640 |
| 10 | Labels | 12486.3120 |
| 11 | Machines | 189238.6310 |
| 12 | Paper | 78479.2060 |
| 13 | Phones | 330007.0540 |
| 14 | Storage | 223843.6080 |
| 15 | Supplies | 46673.5380 |
| 16 | Tables | 206965.5320 |
fig = px.bar(sales_by_subcategory,
x='Sub-Category',
y='Sales',
title='Sales Analysis by Sub-Category')
fig.show()
profit_by_month = data.groupby('Order Month')['Profit'].sum().reset_index()
profit_by_month
| Order Month | Profit | |
|---|---|---|
| 0 | 1 | 9134.4461 |
| 1 | 2 | 10294.6107 |
| 2 | 3 | 28594.6872 |
| 3 | 4 | 11587.4363 |
| 4 | 5 | 22411.3078 |
| 5 | 6 | 21285.7954 |
| 6 | 7 | 13832.6648 |
| 7 | 8 | 21776.9384 |
| 8 | 9 | 36857.4753 |
| 9 | 10 | 31784.0413 |
| 10 | 11 | 35468.4265 |
| 11 | 12 | 43369.1919 |
fig = px.line(profit_by_month,
x='Order Month',
y='Profit',
title='Monthly Profit Analysis')
fig.show()
profit_by_category = data.groupby('Category')['Profit'].sum().reset_index()
profit_by_category
| Category | Profit | |
|---|---|---|
| 0 | Furniture | 18451.2728 |
| 1 | Office Supplies | 122490.8008 |
| 2 | Technology | 145454.9481 |
fig = px.pie(profit_by_category,
values='Profit',
names='Category',
hole=0.5,
color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(title_text='Profit Analysis by Category', title_font=dict(size=24))
fig.show()
profit_by_subcategory = data.groupby('Sub-Category')['Profit'].sum().reset_index()
profit_by_subcategory
| Sub-Category | Profit | |
|---|---|---|
| 0 | Accessories | 41936.6357 |
| 1 | Appliances | 18138.0054 |
| 2 | Art | 6527.7870 |
| 3 | Binders | 30221.7633 |
| 4 | Bookcases | -3472.5560 |
| 5 | Chairs | 26590.1663 |
| 6 | Copiers | 55617.8249 |
| 7 | Envelopes | 6964.1767 |
| 8 | Fasteners | 949.5182 |
| 9 | Furnishings | 13059.1436 |
| 10 | Labels | 5546.2540 |
| 11 | Machines | 3384.7569 |
| 12 | Paper | 34053.5693 |
| 13 | Phones | 44515.7306 |
| 14 | Storage | 21278.8264 |
| 15 | Supplies | -1189.0995 |
| 16 | Tables | -17725.4811 |
fig = px.bar(profit_by_subcategory, x='Sub-Category',
y='Profit',
title='Profit Analysis by Sub-Category')
fig.show()
data.head()
| Row ID | Order ID | Order Date | Ship Date | Ship Mode | Customer ID | Customer Name | Segment | Country | City | ... | Category | Sub-Category | Product Name | Sales | Quantity | Discount | Profit | Order Month | Order Year | Order Day of Week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Bookcases | Bush Somerset Collection Bookcase | 261.9600 | 2 | 0.00 | 41.9136 | 11 | 2016 | 1 |
| 1 | 2 | CA-2016-152156 | 2016-11-08 | 2016-11-11 | Second Class | CG-12520 | Claire Gute | Consumer | United States | Henderson | ... | Furniture | Chairs | Hon Deluxe Fabric Upholstered Stacking Chairs,... | 731.9400 | 3 | 0.00 | 219.5820 | 11 | 2016 | 1 |
| 2 | 3 | CA-2016-138688 | 2016-06-12 | 2016-06-16 | Second Class | DV-13045 | Darrin Van Huff | Corporate | United States | Los Angeles | ... | Office Supplies | Labels | Self-Adhesive Address Labels for Typewriters b... | 14.6200 | 2 | 0.00 | 6.8714 | 6 | 2016 | 6 |
| 3 | 4 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Furniture | Tables | Bretford CR4500 Series Slim Rectangular Table | 957.5775 | 5 | 0.45 | -383.0310 | 10 | 2015 | 6 |
| 4 | 5 | US-2015-108966 | 2015-10-11 | 2015-10-18 | Standard Class | SO-20335 | Sean O'Donnell | Consumer | United States | Fort Lauderdale | ... | Office Supplies | Storage | Eldon Fold 'N Roll Cart System | 22.3680 | 2 | 0.20 | 2.5164 | 10 | 2015 | 6 |
5 rows × 24 columns
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
color_palette = colors.qualitative.Pastel
fig = go.Figure()
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Sales'],
name='Sales',
marker_color=color_palette[0]))
fig.add_trace(go.Bar(x=sales_profit_by_segment['Segment'],
y=sales_profit_by_segment['Profit'],
name='Profit',
marker_color=color_palette[1]))
fig.update_layout(title='Sales and Profit Analysis by Customer Segment',
xaxis_title='Customer Segment', yaxis_title='Amount')
fig.show()
sales_profit_by_segment = data.groupby('Segment').agg({'Sales': 'sum', 'Profit': 'sum'}).reset_index()
sales_profit_by_segment['Sales_to_Profit_Ratio'] = sales_profit_by_segment['Sales'] / sales_profit_by_segment['Profit']
print(sales_profit_by_segment[['Segment', 'Sales_to_Profit_Ratio']])
Segment Sales_to_Profit_Ratio 0 Consumer 8.659471 1 Corporate 7.677245 2 Home Office 7.125416